Alternatives to MySQL logo

Alternatives to MySQL

PostgreSQL, Oracle, MariaDB, MongoDB, and Microsoft SQL Server are the most popular alternatives and competitors to MySQL.
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What is MySQL and what are its top alternatives?

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.
MySQL is a tool in the Databases category of a tech stack.
MySQL is an open source tool with 7K GitHub stars and 2.7K GitHub forks. Here’s a link to MySQL's open source repository on GitHub

Top Alternatives to MySQL

  • PostgreSQL

    PostgreSQL

    PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions. ...

  • Oracle

    Oracle

    Oracle Database is an RDBMS. An RDBMS that implements object-oriented features such as user-defined types, inheritance, and polymorphism is called an object-relational database management system (ORDBMS). Oracle Database has extended the relational model to an object-relational model, making it possible to store complex business models in a relational database. ...

  • MariaDB

    MariaDB

    Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance. ...

  • MongoDB

    MongoDB

    MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding. ...

  • Microsoft SQL Server

    Microsoft SQL Server

    Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions. ...

  • SQLite

    SQLite

    SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file. ...

  • Apache Aurora

    Apache Aurora

    Apache Aurora is a service scheduler that runs on top of Mesos, enabling you to run long-running services that take advantage of Mesos' scalability, fault-tolerance, and resource isolation. ...

  • Cassandra

    Cassandra

    Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL. ...

MySQL alternatives & related posts

PostgreSQL logo

PostgreSQL

63.2K
50.6K
3.5K
A powerful, open source object-relational database system
63.2K
50.6K
+ 1
3.5K
PROS OF POSTGRESQL
  • 754
    Relational database
  • 507
    High availability
  • 436
    Enterprise class database
  • 380
    Sql
  • 302
    Sql + nosql
  • 171
    Great community
  • 145
    Easy to setup
  • 129
    Heroku
  • 128
    Secure by default
  • 111
    Postgis
  • 48
    Supports Key-Value
  • 46
    Great JSON support
  • 32
    Cross platform
  • 29
    Extensible
  • 26
    Replication
  • 24
    Triggers
  • 22
    Rollback
  • 21
    Multiversion concurrency control
  • 20
    Open source
  • 17
    Heroku Add-on
  • 14
    Stable, Simple and Good Performance
  • 13
    Powerful
  • 12
    Lets be serious, what other SQL DB would you go for?
  • 9
    Good documentation
  • 7
    Intelligent optimizer
  • 7
    Scalable
  • 6
    Transactional DDL
  • 6
    Modern
  • 6
    Reliable
  • 5
    Free
  • 5
    One stop solution for all things sql no matter the os
  • 4
    Relational database with MVCC
  • 3
    Full-Text Search
  • 3
    Developer friendly
  • 3
    Faster Development
  • 2
    Excellent source code
  • 2
    search
  • 2
    Great DB for Transactional system or Application
  • 1
    Text
  • 1
    Full-text
  • 1
    Free version
  • 1
    Open-source
CONS OF POSTGRESQL
  • 9
    Table/index bloatings

related PostgreSQL posts

Jeyabalaji Subramanian

Recently we were looking at a few robust and cost-effective ways of replicating the data that resides in our production MongoDB to a PostgreSQL database for data warehousing and business intelligence.

We set ourselves the following criteria for the optimal tool that would do this job: - The data replication must be near real-time, yet it should NOT impact the production database - The data replication must be horizontally scalable (based on the load), asynchronous & crash-resilient

Based on the above criteria, we selected the following tools to perform the end to end data replication:

We chose MongoDB Stitch for picking up the changes in the source database. It is the serverless platform from MongoDB. One of the services offered by MongoDB Stitch is Stitch Triggers. Using stitch triggers, you can execute a serverless function (in Node.js) in real time in response to changes in the database. When there are a lot of database changes, Stitch automatically "feeds forward" these changes through an asynchronous queue.

We chose Amazon SQS as the pipe / message backbone for communicating the changes from MongoDB to our own replication service. Interestingly enough, MongoDB stitch offers integration with AWS services.

In the Node.js function, we wrote minimal functionality to communicate the database changes (insert / update / delete / replace) to Amazon SQS.

Next we wrote a minimal micro-service in Python to listen to the message events on SQS, pickup the data payload & mirror the DB changes on to the target Data warehouse. We implemented source data to target data translation by modelling target table structures through SQLAlchemy . We deployed this micro-service as AWS Lambda with Zappa. With Zappa, deploying your services as event-driven & horizontally scalable Lambda service is dumb-easy.

In the end, we got to implement a highly scalable near realtime Change Data Replication service that "works" and deployed to production in a matter of few days!

See more
Tim Abbott

We've been using PostgreSQL since the very early days of Zulip, but we actually didn't use it from the beginning. Zulip started out as a MySQL project back in 2012, because we'd heard it was a good choice for a startup with a wide community. However, we found that even though we were using the Django ORM for most of our database access, we spent a lot of time fighting with MySQL. Issues ranged from bad collation defaults, to bad query plans which required a lot of manual query tweaks.

We ended up getting so frustrated that we tried out PostgresQL, and the results were fantastic. We didn't have to do any real customization (just some tuning settings for how big a server we had), and all of our most important queries were faster out of the box. As a result, we were able to delete a bunch of custom queries escaping the ORM that we'd written to make the MySQL query planner happy (because postgres just did the right thing automatically).

And then after that, we've just gotten a ton of value out of postgres. We use its excellent built-in full-text search, which has helped us avoid needing to bring in a tool like Elasticsearch, and we've really enjoyed features like its partial indexes, which saved us a lot of work adding unnecessary extra tables to get good performance for things like our "unread messages" and "starred messages" indexes.

I can't recommend it highly enough.

See more
Oracle logo

Oracle

1.5K
1.3K
107
An RDBMS that implements object-oriented features such as user-defined types, inheritance, and polymorphism
1.5K
1.3K
+ 1
107
PROS OF ORACLE
  • 42
    Reliable
  • 31
    Enterprise
  • 15
    High Availability
  • 5
    Hard to maintain
  • 4
    Expensive
  • 4
    Maintainable
  • 3
    High complexity
  • 3
    Hard to use
CONS OF ORACLE
  • 13
    Expensive

related Oracle posts

Hi. We are planning to develop web, desktop, and mobile app for procurement, logistics, and contracts. Procure to Pay and Source to pay, spend management, supplier management, catalog management. ( similar to SAP Ariba, gap.com, coupa.com, ivalua.com vroozi.com, procurify.com

We got stuck when deciding which technology stack is good for the future. We look forward to your kind guidance that will help us.

We want to integrate with multiple databases with seamless bidirectional integration. What APIs and middleware available are best to achieve this? SAP HANA, Oracle, MySQL, MongoDB...

ASP.NET / Node.js / Laravel. ......?

Please guide us

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MariaDB logo

MariaDB

10.9K
8.2K
467
An enhanced, drop-in replacement for MySQL
10.9K
8.2K
+ 1
467
PROS OF MARIADB
  • 149
    Drop-in mysql replacement
  • 100
    Great performance
  • 74
    Open source
  • 54
    Free
  • 44
    Easy setup
  • 15
    Easy and fast
  • 14
    Lead developer is "monty" widenius the founder of mysql
  • 6
    Also an aws rds service
  • 4
    Learning curve easy
  • 4
    Consistent and robust
  • 2
    Native JSON Support / Dynamic Columns
  • 1
    Real Multi Threaded queries on a table/db
CONS OF MARIADB
    Be the first to leave a con

    related MariaDB posts

    Joshua Dean Küpper
    CEO at Scrayos UG (haftungsbeschränkt) · | 11 upvotes · 268.2K views

    We primarily use MariaDB but use PostgreSQL as a part of GitLab , Sentry and Nextcloud , which (initially) forced us to use it anyways. While this isn't much of a decision – because we didn't have one (ha ha) – we learned to love the perks and advantages of PostgreSQL anyways. PostgreSQL's extension system makes it even more flexible than a lot of the other SQL-based DBs (that only offer stored procedures) and the additional JOIN options, the enhanced role management and the different authentication options came in really handy, when doing manual maintenance on the databases.

    See more

    I'm researching what Technology Stack I should use to build my product (something like food delivery App) for Web, iOS, and Android Apps. Please advise which technologies you would recommend from a Scalability, Reliability, Cost, and Efficiency standpoint for a start-up. Here are the technologies I came up with, feel free to suggest any new technology even it's not in the list below.

    For Mobile Apps -

    1. native languages like Swift for IOS and Java/Kotlin for Android
    2. or cross-platform languages like React Native for both IOS and Android Apps

    For UI -

    1. React

    For Back-End or APIs -

    1. Node.js
    2. PHP

    For Database -

    1. PostgreSQL
    2. MySQL
    3. Cloud Firestore
    4. MariaDB

    Thanks!

    See more
    MongoDB logo

    MongoDB

    62.8K
    52.1K
    4.1K
    The database for giant ideas
    62.8K
    52.1K
    + 1
    4.1K
    PROS OF MONGODB
    • 823
      Document-oriented storage
    • 590
      No sql
    • 546
      Ease of use
    • 465
      Fast
    • 405
      High performance
    • 255
      Free
    • 215
      Open source
    • 179
      Flexible
    • 142
      Replication & high availability
    • 109
      Easy to maintain
    • 41
      Querying
    • 37
      Easy scalability
    • 36
      Auto-sharding
    • 35
      High availability
    • 31
      Map/reduce
    • 26
      Document database
    • 24
      Easy setup
    • 24
      Full index support
    • 15
      Reliable
    • 14
      Fast in-place updates
    • 13
      Agile programming, flexible, fast
    • 11
      No database migrations
    • 7
      Enterprise
    • 7
      Easy integration with Node.Js
    • 5
      Enterprise Support
    • 4
      Great NoSQL DB
    • 3
      Aggregation Framework
    • 3
      Support for many languages through different drivers
    • 3
      Drivers support is good
    • 2
      Schemaless
    • 2
      Fast
    • 2
      Awesome
    • 2
      Managed service
    • 2
      Easy to Scale
    • 1
      Consistent
    CONS OF MONGODB
    • 5
      Very slowly for connected models that require joins
    • 3
      Not acid compliant
    • 1
      Proprietary query language

    related MongoDB posts

    Jeyabalaji Subramanian

    Recently we were looking at a few robust and cost-effective ways of replicating the data that resides in our production MongoDB to a PostgreSQL database for data warehousing and business intelligence.

    We set ourselves the following criteria for the optimal tool that would do this job: - The data replication must be near real-time, yet it should NOT impact the production database - The data replication must be horizontally scalable (based on the load), asynchronous & crash-resilient

    Based on the above criteria, we selected the following tools to perform the end to end data replication:

    We chose MongoDB Stitch for picking up the changes in the source database. It is the serverless platform from MongoDB. One of the services offered by MongoDB Stitch is Stitch Triggers. Using stitch triggers, you can execute a serverless function (in Node.js) in real time in response to changes in the database. When there are a lot of database changes, Stitch automatically "feeds forward" these changes through an asynchronous queue.

    We chose Amazon SQS as the pipe / message backbone for communicating the changes from MongoDB to our own replication service. Interestingly enough, MongoDB stitch offers integration with AWS services.

    In the Node.js function, we wrote minimal functionality to communicate the database changes (insert / update / delete / replace) to Amazon SQS.

    Next we wrote a minimal micro-service in Python to listen to the message events on SQS, pickup the data payload & mirror the DB changes on to the target Data warehouse. We implemented source data to target data translation by modelling target table structures through SQLAlchemy . We deployed this micro-service as AWS Lambda with Zappa. With Zappa, deploying your services as event-driven & horizontally scalable Lambda service is dumb-easy.

    In the end, we got to implement a highly scalable near realtime Change Data Replication service that "works" and deployed to production in a matter of few days!

    See more
    Robert Zuber

    We use MongoDB as our primary #datastore. Mongo's approach to replica sets enables some fantastic patterns for operations like maintenance, backups, and #ETL.

    As we pull #microservices from our #monolith, we are taking the opportunity to build them with their own datastores using PostgreSQL. We also use Redis to cache data we’d never store permanently, and to rate-limit our requests to partners’ APIs (like GitHub).

    When we’re dealing with large blobs of immutable data (logs, artifacts, and test results), we store them in Amazon S3. We handle any side-effects of S3’s eventual consistency model within our own code. This ensures that we deal with user requests correctly while writes are in process.

    See more
    Microsoft SQL Server logo

    Microsoft SQL Server

    12.7K
    9.3K
    535
    A relational database management system developed by Microsoft
    12.7K
    9.3K
    + 1
    535
    PROS OF MICROSOFT SQL SERVER
    • 137
      Reliable and easy to use
    • 101
      High performance
    • 94
      Great with .net
    • 65
      Works well with .net
    • 56
      Easy to maintain
    • 21
      Azure support
    • 17
      Always on
    • 17
      Full Index Support
    • 10
      Enterprise manager is fantastic
    • 9
      In-Memory OLTP Engine
    • 2
      Security is forefront
    • 1
      Columnstore indexes
    • 1
      Great documentation
    • 1
      Faster Than Oracle
    • 1
      Decent management tools
    • 1
      Easy to setup and configure
    • 1
      Docker Delivery
    CONS OF MICROSOFT SQL SERVER
    • 4
      Expensive Licensing
    • 2
      Microsoft

    related Microsoft SQL Server posts

    We initially started out with Heroku as our PaaS provider due to a desire to use it by our original developer for our Ruby on Rails application/website at the time. We were finding response times slow, it was painfully slow, sometimes taking 10 seconds to start loading the main page. Moving up to the next "compute" level was going to be very expensive.

    We moved our site over to AWS Elastic Beanstalk , not only did response times on the site practically become instant, our cloud bill for the application was cut in half.

    In database world we are currently using Amazon RDS for PostgreSQL also, we have both MariaDB and Microsoft SQL Server both hosted on Amazon RDS. The plan is to migrate to AWS Aurora Serverless for all 3 of those database systems.

    Additional services we use for our public applications: AWS Lambda, Python, Redis, Memcached, AWS Elastic Load Balancing (ELB), Amazon Elasticsearch Service, Amazon ElastiCache

    See more

    I am a Microsoft SQL Server programmer who is a bit out of practice. I have been asked to assist on a new project. The overall purpose is to organize a large number of recordings so that they can be searched. I have an enormous music library but my songs are several hours long. I need to include things like time, date and location of the recording. I don't have a problem with the general database design. I have two primary questions:

    1. I need to use either MySQL or PostgreSQL on a Linux based OS. Which would be better for this application?
    2. I have not dealt with a sound based data type before. How do I store that and put it in a table? Thank you.
    See more
    SQLite logo

    SQLite

    11.9K
    9.3K
    528
    A software library that implements a self-contained, serverless, zero-configuration, transactional SQL database engine
    11.9K
    9.3K
    + 1
    528
    PROS OF SQLITE
    • 160
      Lightweight
    • 134
      Portable
    • 121
      Simple
    • 80
      Sql
    • 28
      Preinstalled on iOS and Android
    • 2
      Tcl integration
    • 1
      Free
    • 1
      Telefon
    • 1
      Portable A database on my USB 'love it'
    CONS OF SQLITE
    • 2
      Not for multi-process of multithreaded apps
    • 1
      Needs different binaries for each platform

    related SQLite posts

    Dimelo Waterson
    Shared insights
    on
    PostgreSQL
    MySQL
    SQLite

    I need to add a DBMS to my stack, but I don't know which. I'm tempted to learn SQLite since it would be useful to me with its focus on local access without concurrency. However, doing so feels like I would be defeating the purpose of trying to expand my skill set since it seems like most enterprise applications have the opposite requirements.

    To be able to apply what I learn to more projects, what should I try to learn? MySQL? PostgreSQL? Something else? Is there a comfortable middle ground between high applicability and ease of use?

    See more
    Christian Stefanescu
    Head of IT at lawpilots · | 3 upvotes · 4.1K views
    Shared insights
    on
    Django
    SQLite
    PostgreSQL

    While I love and use PostgreSQL , I would definitely recommend having a look at SQLite as well. It can be a solid database for lots of applications and it brings some advantages in terms of handling: you don't need a server running, which makes things like testing, deploying or backing up databases much easier. Through the ORM in Django you are one abstraction level away from your database anyway and switching later on is definitely an option, but I believe SQLite is very good for pretty much all the small applications you can think of.

    See more
    Apache Aurora logo

    Apache Aurora

    61
    79
    0
    An Apcahe Mesos framework for scheduling jobs, originally developed by Twitter
    61
    79
    + 1
    0
    PROS OF APACHE AURORA
      Be the first to leave a pro
      CONS OF APACHE AURORA
        Be the first to leave a con

        related Apache Aurora posts

        Docker containers on Mesos run their microservices with consistent configurations at scale, along with Aurora for long-running services and cron jobs.

        See more
        Cassandra logo

        Cassandra

        3.2K
        3.1K
        486
        A partitioned row store. Rows are organized into tables with a required primary key.
        3.2K
        3.1K
        + 1
        486
        PROS OF CASSANDRA
        • 112
          Distributed
        • 93
          High performance
        • 80
          High availability
        • 74
          Easy scalability
        • 52
          Replication
        • 26
          Reliable
        • 26
          Multi datacenter deployments
        • 8
          OLTP
        • 7
          Schema optional
        • 6
          Open source
        • 2
          Workload separation (via MDC)
        CONS OF CASSANDRA
        • 2
          Reliability of replication
        • 1
          Updates

        related Cassandra posts

        Thierry Schellenbach
        Shared insights
        on
        Redis
        Cassandra
        RocksDB
        at

        1.0 of Stream leveraged Cassandra for storing the feed. Cassandra is a common choice for building feeds. Instagram, for instance started, out with Redis but eventually switched to Cassandra to handle their rapid usage growth. Cassandra can handle write heavy workloads very efficiently.

        Cassandra is a great tool that allows you to scale write capacity simply by adding more nodes, though it is also very complex. This complexity made it hard to diagnose performance fluctuations. Even though we had years of experience with running Cassandra, it still felt like a bit of a black box. When building Stream 2.0 we decided to go for a different approach and build Keevo. Keevo is our in-house key-value store built upon RocksDB, gRPC and Raft.

        RocksDB is a highly performant embeddable database library developed and maintained by Facebook’s data engineering team. RocksDB started as a fork of Google’s LevelDB that introduced several performance improvements for SSD. Nowadays RocksDB is a project on its own and is under active development. It is written in C++ and it’s fast. Have a look at how this benchmark handles 7 million QPS. In terms of technology it’s much more simple than Cassandra.

        This translates into reduced maintenance overhead, improved performance and, most importantly, more consistent performance. It’s interesting to note that LinkedIn also uses RocksDB for their feed.

        #InMemoryDatabases #DataStores #Databases

        See more
        Umair Iftikhar
        Technical Architect at Vappar · | 3 upvotes · 121.7K views

        Developing a solution that collects Telemetry Data from different devices, nearly 1000 devices minimum and maximum 12000. Each device is sending 2 packets in 1 second. This is time-series data, and this data definition and different reports are saved on PostgreSQL. Like Building information, maintenance records, etc. I want to know about the best solution. This data is required for Math and ML to run different algorithms. Also, data is raw without definitions and information stored in PostgreSQL. Initially, I went with TimescaleDB due to PostgreSQL support, but to increase in sites, I started facing many issues with timescale DB in terms of flexibility of storing data.

        My major requirement is also the replication of the database for reporting and different purposes. You may also suggest other options other than Druid and Cassandra. But an open source solution is appreciated.

        See more